Results_Plot.ipynb

{"cells":[{"cell_type":"code","metadata":{"deepnote_cell_type":"code","tags":[],"cell_id":"00001-ef438646-be83-45f9-8974-ef932bf751dd","output_cleared":false,"source_hash":"84e8905d","execution_millis":698,"execution_start":1606621414311},"source":"from sklearn.linear_model import LogisticRegression # importing Sklearn's logistic regression's module\nfrom sklearn import preprocessing, linear_model # preprossing is what we do with the data before we run the learning algorithm\nfrom sklearn.model_selection import train_test_split \nfrom sklearn.metrics import accuracy_score, precision_recall_fscore_support # \nimport numpy as np\nimport pandas as pd\nimport math\n\n\nimport matplotlib.pyplot as plt\n%matplotlib inline","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"deepnote_cell_type":"code","tags":[],"cell_id":"00001-d81a67e4-7b32-487e-bb3e-86904ff5ca75","output_cleared":false,"source_hash":"9d203e2","execution_millis":177,"execution_start":1606621439733},"source":"#Graphing \n\n#Logistic / Pca Log/ SVM / SVMPCA\nacc = [0.9807692307692307, 0.754615, 0.9838461538461538, 0.7353846153846154]\nprecision = [0.9866803278688525, 0.7551963048498845, 0.9917525773195877, 0.750392464678179]\nf1score = [0.9871860584315736, 0.8601490574309514, 0.9892030848329049, 0.8475177304964541]\nrecall = [0.9876923076923076, 0.9989816700610998, 0.9866666666666667, 0.9735234215885947]\n\n\nx = np.arange(4)\n\nfig, ax = plt.subplots(figsize=(11,7))\nax.bar(x-0.4, acc, width=0.18, align='edge',label='accuracy', color = 'plum')\nax.bar(x-0, precision, width=0.18, align='edge',label='precision', color = 'deepskyblue')\nax.bar(x-0.2, recall, width=0.18, align='edge',label='recall', color = 'aquamarine')\nax.bar(x+0.2, f1score, width=0.18, align='edge',label='f1 score', color = 'darksalmon')\n\nax.set_ylabel('Score')\nax.set_ylim([0.1, 1.02])\nax.set_title('Scores by Classifier')\nax.set_xticks(x)\nax.set_xticklabels(('Logistic Regression', 'Logistic Regression with PCA', 'SVM', 'SVM with PCA'))\nax.legend()\n","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"<matplotlib.legend.Legend at 0x7f5b8c42b4d0>"},"metadata":{}},{"data":{"text/plain":"<Figure size 792x504 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"deepnote_cell_type":"code","tags":[],"cell_id":"00002-0ab5b9c6-1d27-477d-b52a-d3e3a33e73c4","output_cleared":false,"source_hash":"d3bf8ba3","execution_millis":394,"execution_start":1606621415215},"source":"from sklearn import svm\nfrom sklearn.metrics import plot_confusion_matrix\n\n# Confusion Matrix using SVM classifier\ndf = pd.read_csv('winequality-combined.csv')\ndf1 = df.dropna()\ndf2 = df1.values\nX = df1[['fixed acidity','volatile acidity','citric acid',\n'residual sugar','chlorides','free sulfur dioxide','total sulfur dioxide','sulphates']].values\ny = df2[:, 12]\nclass_names = ['red','white']\n#Splitting the data into train/test (80%/20%)\nx_scale = preprocessing.scale(X)\nx_train, x_test, y_train, y_test = train_test_split(x_scale, y, test_size = 0.20, train_size=0.80)\nclassifier = svm.SVC(kernel='linear', C=0.01).fit(x_train, y_train)\n\nnp.set_printoptions(precision=2)\n\n# Plot non-normalized confusion matrix\ntitles_options = [(\"Confusion matrix, without normalization\", None),\n                  (\"Normalized confusion matrix\", 'true')]\nfor title, normalize in titles_options:\n    disp = plot_confusion_matrix(classifier, x_test, y_test,\n                                 display_labels=class_names,\n                                 cmap=plt.cm.Blues,\n                                 normalize=normalize)\n    disp.ax_.set_title(title)\n\n    print(title)\n    print(disp.confusion_matrix)\n\nplt.show()\n\n","execution_count":null,"outputs":[{"name":"stdout","text":"Confusion matrix, without normalization\n[[304   8]\n [  6 982]]\nNormalized confusion matrix\n[[0.97 0.03]\n [0.01 0.99]]\n","output_type":"stream"},{"data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"deepnote_cell_type":"code","tags":[],"cell_id":"00004-bcfbe190-172c-4767-95f0-7aef511c93bc","output_cleared":false,"source_hash":"9cd83363","execution_millis":579,"execution_start":1606621415616},"source":"# Confusion Matrix using logistic regression classifier\ndf = pd.read_csv('winequality-combined.csv')\ndf1 = df.dropna()\ndf2 = df1.values\nX = df1[['fixed acidity','volatile acidity','citric acid',\n'residual sugar','chlorides','free sulfur dioxide','total sulfur dioxide','sulphates']].values\ny = df2[:, 12]\nclass_names = ['red','white']\n#Splitting the data into train/test (80%/20%)\nx_scale = preprocessing.scale(X)\nx_train, x_test, y_train, y_test = train_test_split(x_scale, y, test_size = 0.20, train_size=0.80)\nclassifier = linear_model.LogisticRegression(C=10**8).fit(x_train,y_train)\n\nnp.set_printoptions(precision=2)\n\n# Plot non-normalized confusion matrix\ntitles_options = [(\"Confusion matrix, without normalization\", None),\n                  (\"Normalized confusion matrix\", 'true')]\nfor title, normalize in titles_options:\n    disp = plot_confusion_matrix(classifier, x_test, y_test,\n                                 display_labels=class_names,\n                                 cmap=plt.cm.Blues,\n                                 normalize=normalize)\n    disp.ax_.set_title(title)\n\n    print(title)\n    print(disp.confusion_matrix)\n\nplt.show()","execution_count":null,"outputs":[{"name":"stdout","text":"Confusion matrix, without normalization\n[[301  11]\n [  7 981]]\nNormalized confusion matrix\n[[0.96 0.04]\n [0.01 0.99]]\n","output_type":"stream"},{"data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"deepnote_cell_type":"code","tags":[],"cell_id":"00005-18ca0a26-2576-4850-883e-ded6259488ab","output_cleared":false,"source_hash":"b623e53d","execution_start":1606621416198,"execution_millis":44},"source":"","execution_count":null,"outputs":[]}],"nbformat":4,"nbformat_minor":2,"metadata":{"orig_nbformat":2,"deepnote_notebook_id":"06412786-a777-4799-b9f5-9694db95f252","deepnote_execution_queue":[]}}